Certain imaging systems image electromagnetic energy emitted by or reflected from an object through optics, capture a digital image of the object, and process the digital image to enhance image quality or alter characteristics of the digital image. Processing may require significant computational resources such as memory space and computing time to enhance image quality.
For human viewers, image quality is a subjective qualification of the properties of an image. For machine vision applications, image quality is related to a degree to which an image is usable in the performance of a task. Processing of electronic image data can improve image quality based either on subjective or objective factors. For example, human viewers might consider subjective factors such as sharpness, brightness, contrast, colorfulness, noisiness, discriminability, identifiability and naturalness. Sharpness describes the presence of fine detail; for example, a human viewer might expect to see individual blades of grass. Brightness describes overall lightness or darkness of an image; for example, a sunny outdoor scene is considered bright whereas a shadowed indoor scene is considered dark. Contrast describes a difference in lightness between lighter and darker regions of an image. Colorfulness describes intensity of hue of colors; for example, a gray color has low colorfulness, while a vivid red has high colorfulness. Noisiness describes a degree to which noise is present. Noise is introduced, for instance, by an image detector (e.g., as fixed pattern noise, temporal noise, or as effects of defective pixels of the detector) or by image manipulating algorithms (e.g., uniformity defects). Discriminability describes an ability to distinguish objects in an image from each other. Identifiability describes a degree to which an image or portion thereof conforms to a human viewer's association of the image or a similar image. Naturalness describes a degree to which an image or portions thereof match a human viewer's idealized memory of that image or portion; for example, green grass, blue skies and tan skin are considered more natural the closer that they are perceived to the idealized memory of that particular human viewer.
For machine vision applications, image quality is related to a degree to which an image is appropriate for use in performing a particular task. The quality of an image associated with a machine vision application can be related to a certain value of a signal-to-noise ratio (SNR) and a probability of successfully completing a certain task. For example, in a package sorting system, images of packages are utilized to identify the edges of each package to determine package sizes. If the sorting system is able to consistently identify packages, then a probability of success is high and therefore a SNR for edges in the utilized images is considered sufficient for performing the task. For iris recognition, specific spatial frequencies of features within an iris can be identified to support discrimination between irises. If an SNR for these spatial frequencies is insufficient, then an iris recognition algorithm will not function as desired.